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This paper aims to identify and analyze the challenges for the Varanasi handloom industry after the COVID pandemic by considering their impact on different sections of the weavers and subsequently suggest the best possible solution for the same.

A combined approach of expert opinion and in-depth literature reviews are used to identify the challenges, and a multicriteria decision-making tool is used to rank the challenges for the type of weaver.

This research provides an elaborated view of the problems faced by the handloom industry after the COVID pandemic and suggests that the success of the handloom business is subjected to the eradication of a wide number of challenges according to the type of weaver.

The findings of this research will help the policymakers to make and align their policies and strategies for the upliftment of the Varanasi handloom industry efficiently and effectively.

To the best of the authors’ knowledge, this is the first kind of study that focuses on identifying and prioritizing the barriers affecting the success of the Varanasi handloom industry after the COVID pandemic. Furthermore, the uniqueness of this research lies in its ability to study all three independent sections of the handloom industry, having different capabilities and limitations.

This study aims to examine the factors that impact the choice of paddy marketing channels in India at the farm level and household contingencies.

Employing multinomial logistic regression, the analysis utilizes the National Sample Survey Office (NSSO) 77th round Situation Assessment Survey (SAS) data from the 2018 to 2019 period, specifically for the paddy Kharif season, to determine the factors determining the choice of marketing channels. The significant independent variables include minimum support price (MSP) awareness, access to and adoption of technical advice, input agency, social group, farm size of farmers, region, age and education of the household head.

Awareness of MSP and adoption of technical advice from experts can enhance the probability of selecting government channels for paddy. The reliance on government input agencies has a favourable impact on the choice of government channels. Government channels are more likely preferred by higher social groups and those with higher land-holdings. There has been a state-wise variation in access to regulated marketing channels for paddy.

Transaction cost associated with marketing channel choice is an important factor, not incorporated in this study due to the unavailability in the NSS data.

The research uses the latest unit-level data of the NSSO 77th round, published by the Ministry of Statistics and Programme Implementation (MoSPI), the Government of India.

Blockchain is a nascent technology that has the ability to revolutionize the workings of the educational landscape. However, there are several barriers to the adoption of blockchain technology (BCT). So, this paper aims to determine, rank and analyse the interdependent contextual relationship among the BCT adoption barriers within the education management system (EMS) of higher education institutes.

The present research principally uses the technology-organization-environment model for the classification of BCT adoption barriers in the EMS. An integrated dual phase best-worst method and interpretive structural modelling – cross-impact matrix multiplication applied to classification (BWM-ISM-MICMAC) analysis is used for the identification, prioritization and analysis of the contextual relationships among the BCT barriers.

The findings of the research show that the environmental barrier among the primary barriers and the lack of government initiatives among the sub-barriers are crucial barriers to BCT adoption in the EMS.

Certain barriers might not be selected during the literature review and expert opinions might be biased. Future studies should use structural equation modelling to validate the relationships between BCT barriers and conduct similar research across other business sectors to provide valuable insights for scholars and practitioners alike.

The study facilitates a better perspective of BCT barriers and explores possible solutions more comprehensively for policymakers and field managers. Consequently, it offers viable suggestions for the successful and effective implementation of BCT within the EMS.

The concept of using the blockchain in academics is a novel one. This study establishes a crucial research base for the smooth adoption of BCT in EMS.

Accurate 6D object pose estimation is essential for various robotic tasks. Uncertain pose estimates can lead to task failures; however, a certain degree of error in the pose estimates is often acceptable. This paper aims to enable the robots to make informed decisions by quantifying errors in the object pose estimate and acceptable errors for task success.

In this paper, the authors introduce a framework for evaluating robotic task success under object pose uncertainty, representing both the estimated error space of the object pose and the acceptable error space for task success using multi-modal non-parametric probability distributions. The proposed framework pre-computes the acceptable error space for task success using dynamic simulations and subsequently integrates the pre-computed acceptable error space over the estimated error space of the object pose to predict the likelihood of the task succes.

The authors evaluated the proposed framework on two mobile manipulation tasks. Their results show that by representing the estimated and the acceptable error space using multi-modal non-parametric distributions, the authors achieve higher task success rates and fewer failures.

Their proposed framework is generic and can be applied to a wide range of robotic tasks requiring object pose estimation. Hence, given the recent advancements in object pose uncertainty estimation and dynamic simulations, the proposed framework, in conjunction with these advancements, has the potential to enable robots to make reliable and informed decisions under pose uncertainty.

Unlike related works that model both acceptable error space and estimated error space using parametric uni-modal distributions, the authors model them as multi-modal distributions which is often the case in the real world.

As the search engine platform, Baidu has already developed keyword advertising as one of its main business scopes, while in-feed advertising is emerging as another intelligent choice for the company. Our purpose is to validate the effectiveness of keyword and retargeted in-feed advertising on offline sales and whether the effectiveness of these two advertising strategies relies on keyword attributes work.

We utilize data from the ad campaigns of a prominent manufacturer within the machinery and equipment (hereinafter referred to as “the company”) on Baidu. To scrutinize the research hypotheses, we have employed linear regression models. Subsequently, we address potential endogeneity issues and use various techniques to ascertain the reliability of the results.

Empirical evidence indicates that both keyword and in-feed advertising enhance offline sales. Upon examining the moderating role of keyword attributes (specificity and length), we observe that specific keywords (price and word-of-mouth (WOM)) accelerate the boosting effect of advertising on sales; similarly, the longer the keywords, the more obvious the enhanced impact of advertising on sales. Moreover, the positive influence of specific keywords (price and WOM) on advertising effectiveness is more outstanding when the keywords are longer.

To our knowledge, no empirical investigation has yet to analyze keyword and retargeted in-feed advertising concurrently within the search engine context. Our research is the inaugural work to reveal that they serve as mutual substitutes regarding their impact on sales. Furthermore, this paper pioneers examining the moderating effects exerted by keyword attributes (specificity and length) on the effectiveness of these two ad types. The findings presented herein offer valuable insights into the harmonious coexistence and collaboration among companies, advertisers, users and search engine platforms.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

Developing countries are characterized by gender wage inequality that can be largely attributed to gender-based disparities in education. Education subsidy can be an effective tool for reducing the inequality in human capital formation. However, the parents’ decision in response to the subsidy is a crucial determinant of gendered inequality in schooling and earnings. The paper aims to examine the effects of gender-neutral and gender-specific education subsidy policies on the gendered differences in schooling and earnings.

A three-sector full employment general equilibrium model is developed, where the amount of schooling of children is determined by the intertemporal utility maximizing behaviour of the parents over two periods.

The results indicate that higher gender-neutral education subsidy may raise the amount of schooling of boys more than girls and aggravate the schooling inequality in a society with traditional gender norms; the effect on earning inequality depends on the relative gendered returns to education and the marginal effects of the subsidy on relative schooling levels. However, gender-specific subsidy policies raise female schooling, thereby narrowing gender-based schooling and are likely to favourably affect the earning inequality.

The paper tries to analyse the linkage between education and labour market within a gender dimension. On the one hand, it tries to explain parental schooling decision due to gender-neutral and gender-specific education subsidies, and on the other, it examines the effects of these two subsidy policies on gendered schooling inequality and gender earnings inequality in a dynamic model.

Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

This study developed a theoretical model to test the relationship between digital capability and Industry 4.0 (I4.0) and its effect on innovation performance in small and medium-sized enterprises (SMEs).

The proposed theoretical model was evaluated using partial least-squares structural equation modeling and fuzzy-set qualitative comparative analysis. The data were obtained from a sample of 536 SMEs in Chile.

The proposed model presented two dimensions of digital capability: management and information and communication technologies (ICTs). Management models composed of enterprise resource planning and customer relationship management systems are essential for optimizing organizational management. Meanwhile, ICTs facilitate the smooth flow of information within an organization, leading to improved efficiency in production processes. I4.0 is encouraged by exposing SMEs to base technologies such as data analytics. These results confirm that I4.0 influences innovation performance.

SME managers should encourage the development of digital capabilities to transition toward I4.0, as this can make SMEs more competitive and innovative in changing and dynamic scenarios.

I4.0 adoption and the development of digital capabilities can directly affect employment and national economic growth.

Most studies focus on the organizational factors affecting SMEs’ I4.0 adoption. They do not, however, address the role played by current digital capability in I4.0 technology adoption and its effect on firms’ innovation performance.

Este estudio desarrolló un modelo teórico para probar la relación entre la capacidad digital y la Industria 4.0 (I4.0) y su efecto en el desempeño de la innovación en pequeñas y medianas empresas (PYME).

El modelo teórico propuesto se evaluó mediante el uso de modelos de ecuaciones estructurales de mínimos cuadrados parciales y análisis comparativo cualitativo de conjuntos difusos. Los datos se obtuvieron de una muestra de 536 pymes de Chile.

El modelo propuesto presenta dos dimensiones de la capacidad digital: la gestión y las tecnologías de la información y la comunicación (TIC). Los modelos de gestión compuestos por sistemas de planificación de recursos empresariales y de gestión de relaciones con los clientes son esenciales para optimizar la gestión organizacional. Por su parte, las TIC facilitan el flujo fluido de información dentro de una organización, lo que conduce a una mejora de la eficiencia en los procesos de producción. La I4.0 se fomenta exponiendo a las PYME a tecnologías de base como el análisis de datos. Estos resultados confirman que la I4.0 influye en el rendimiento de la innovación.

La mayoría de los estudios se centran en los factores organizativos que afectan a la adopción de la I4.0 por parte de las pymes, pero no abordan el papel que desempeña la capacidad digital actual en la adopción de la tecnología I4.0 y su efecto en el desempeño innovador de las empresas.

Los gestores de las PYMES deben incentivar el desarrollo de capacidades digitales para realizar la transición hacia la I4.0, ya que esto puede hacer que las PYMES sean más competitivas e innovadoras en escenarios cambiantes y dinámicos.

La adopción de la I4.0 y el desarrollo de capacidades digitales pueden afectar directamente al empleo y al crecimiento económico nacional.